METHOD FOR CARRYING OUT A DRILLING OPERATION, VIBRATION ANALYZER AND DRILLING SYSTEM

Abstract

The disclosure provides a solution to problems associated with vibrations during drilling by applying post-job data metrics and incorporating insights from previous experiences into pre-job planning for drilling operations. Vibration information and analysis can also be used during an ongoing drilling operation, where a vibration source can be automatically identified and operating parameters can be adjusted to mitigate the vibration. The disclosure outlines a method for conducting a drilling operation, a vibration analyzer, and a drilling system.In one example, the method includes: (1) collecting drilling job data from a completed drilling job, where the drilling job data includes sensor data collected from downhole sensors, (2) determining a vibration severity index from the sensor data, and (3) conducting at least a portion of a drilling operation based on the vibration severity index.

Description

DESIGN OF SERVICE IMPROVEMENTS THROUGH THE USE OF ADAPTABLE MODELS DERIVED FROM CLASSIFIED VIBRATION MECHANISMS BACKGROUND

[0001] Access to hydrocarbon reserves, such as gas or oil reserves, generally involves creating a well by drilling into the earth with a drill bit. The drill bit is part of a bottom hole assembly (BHA), located at the downhole end of a drill string, which includes multiple drill pipes connected together. In addition to the drill bit, the BHA includes other components such as stabilizers, a drill collar, gauges or sensors, and directional drilling equipment. A top control unit at the wellhead is used to rotate the drill string, which rotates the drill bit and extends the well into the earth. A major factor affecting drilling performance is the severity and types of vibrations encountered by the BHA and other downhole tools during drilling operations. SYNTHESIS

[0002] In one aspect, the disclosure provides a method for carrying out a drilling operation. In one example, the method includes: (1) collecting drill job data from a completed drill job, wherein the drill job data includes sensor data collected from downhole sensors, (2) determining a vibration severity index from the sensor data, and (3) carrying out at least a portion of a drilling operation based on the vibration severity index.

[0003] In another aspect, the disclosure provides a vibration analyzer. In one example, the vibration analyzer includes: (1) a memory that has drill job data from at least one drill job, wherein the drill job data includes sensor data from the drill job, and (2) a processor configured to generate vibration information from sensor data, generate a dataset from a data lake of the drill job data and vibration information, extract at least one adaptive model from the dataset, and use the at least one adaptive model to carry out a drilling operation.

[0004] In another aspect, disclosure provides a drilling system. In a 1970156 of 18 example, the drilling system includes: (1) multiple downhole tools, and (2) a processor configured to direct the operation of at least one of the multiple tools for a drilling operation based on at least one adaptive model extracted from a dataset based on a data lake, wherein the data lake includes drilling job data from a completed drilling job and vibration information generated from the completed drilling job. BRIEF DESCRIPTION

[0005] Reference is now made to the following descriptions taken in conjunction with the accompanying drawings, in which:

[0006] Figure 1 illustrates a system diagram of an example of a drilling system configured to carry out formation drilling to create a well in accordance with the disclosure principles;

[0007] Figure 2 illustrates a flowchart of an example of a method for carrying out a drilling operation conducted in accordance with the principles of disclosure;

[0008] Figure 3 illustrates a flowchart of an example of a method for determining a vibration severity index carried out in accordance with the disclosure principles;

[0009] Figure 4 illustrates an example of a vibration mechanism index of a dataset generated from a data pool in accordance with disclosure principles; and

[0010] Figure 5 illustrates a block diagram of an example of a vibration analyzer constructed in accordance with the disclosure principles. DETAILED DESCRIPTION

[0011] The disclosure acknowledges that the impact of vibrations from drilling can be correlated with particular components associated with the drilling operation, such as the BHA assembly, the type of formation being drilled, and the drilling parameters used. By definition, the disclosure provides a system and method that classifies vibrations and then uses the vibration classifications for service design (DOS), such as BHA design, drill bit selection, or drilling parameters to be used during drilling. The disclosure provides a solution to problems associated with vibrations during drilling by applying post-job data metrics and providing inputs from 2 1970156 of 18 previous experiences in pre-job planning for drilling operations. The disclosed system and method collect downhole sensor data in conjunction with other job-related data and use physical or vibration mode-based data, severity, and statistical indices to link DOS recommendations and provide DOS effectiveness from a vibration perspective. By definition, the design of tools and services, such as determining the BHA configuration that will give the best performance within a region, can be improved. This can result in a reduction of unproductive time. Valid tool and service designs can also be verified for future use. Consequently, the service design components for a drilling operation can be selected based on vibration information from previous drilling jobs.

[0012] For example, drilling engineers can design a service for a drilling job based on certain components requested by a client. Multiple types of components may be available and can be selected based on vibration information. Considering gamma-ray sensors as an example, there may be three or four different types to choose from, but perhaps one of them causes more vibration in the particular drilling operation area. By definition, a different gamma-ray sensor can be selected for the drilling operation. Furthermore, a particular gamma-ray sensor may introduce less vibration in the specific area and be combined with other components for the drilling operation. By definition, this particular gamma-ray sensor can be selected for the drilling operation based on vibration information.

[0013] The logic for an example of using vibration information from one or more completed drilling jobs for another drilling operation, as disclosed herein, is illustrated in the flowcharts in Figures 2 and 3. This logic may represent algorithms and may reside in a computer system and / or a vibration analyzer, as mentioned in Figure 1 and depicted in Figure 5. The logic may be implemented as a computer program that has a series of operational instructions stored on a non-transient, computer-readable medium, which directs a data processing apparatus when executed to perform operations. In addition to well processing, the algorithms may be coded into a tool for dynamic analysis and operational changes. 1970156 of 18

[0014] Figure 1 illustrates a drilling system 100 configured to perform formation drilling to create a well 101. The system 100 may be, for example, a logging-while-drilling (LWD) system or a measurement-while-drilling (MWD) system. Figure 1 represents an onshore operation. Persons of intermediate skill will understand that the disclosure is equally suitable for use in marine operations and onshore operations in a body of water. The system 100 includes a bottom hole assembly (BHA) 120 comprising a drill bit 110 that is operatively coupled to a tool string 150, which can be moved axially within the well 101. During the operation, the drill bit 110 penetrates the ground 102 and creates the well 101. The BHA 120 provides directional control of the drill bit 110 as it advances into the ground 102.The 150 tool string can be semi-permanently mounted with various measuring tools (not shown) such as, but not limited to, MWD and LWD tools, which can be configured to take downhole measurements of drilling conditions and the geological formation of the earth 102. The measuring tools may include sensors such as magnetometers, accelerometers, gyroscopes, etc.

[0015] System 100 is configured to activate the BHA 120 positioned or otherwise placed at the bottom of the drill string 125 extended in the ground 102 from a derrick 130 positioned at the surface 104. System 100 includes an upper drive unit 131 used to rotate the drill string 125 at the surface 104, which then rotates the drill bit 110 in the well 101. Operation of the upper drive 131 is controlled by an upper drive unit controller. System 100 may also include a drill stem and a movable block used to lower and raise the drill stem and drill string 125.

[0016] Drilling fluid or mud from a mud tank 140 can be pumped to the bottom of the well using a mud pump 142 powered by an adjacent power source, such as a motive force or motor 144. Drilling mud can be pumped from a mud tank 140, through a riser pipe 146, which feeds the drilling mud into the drill string 125 and also carries it to the drill bit 110. The drilling mud releases one or more nozzles of the drill bit 110 and in the process cools the drill bit 110. After exiting the drill bit 110, the mud circulates back to the surface 104 through the annular space between the wellbore 101 and the drill string 4 1970156 of 18 drilling 125, and in the process, returns the drill cuttings and debris to the surface. The mixture of cuttings and mud is passed through a flow line 148 and processed in such a way that clean mud is returned to the bottom of the well through the riser pipe 146.

[0017] System 100 also includes a well site controller 160, and a computer system 164, which can be communicatively coupled to the well site controller 160. The well site controller 160 includes a processor and memory and is configured to direct the operation of System 100.

[0018] The well site controller 160 or the computer system 164 can be used to communicate with the downhole tools of the tool string 150, such as sending and receiving telemetry, data, drilling sensor data, instructions, and other information, including, but not limited to, collected or measured parameters, location within the well 101, and cuttings information. A communication channel can be established by using, for example, electrical signals or mud pulse telemetry for most of the length of the tool string 150 from drill bit 110 to the controller 160.

[0019] The controller 160, or a separate computing device such as the computer system 164 or a processor located with the BHA 120 or the tool string 150, can be configured to perform one or more of the functions of a vibration analyzer as disclosed herein. For example, the controller 160, the computer system 164, or a combination thereof can be configured to determine adaptive models from drill job data. The adaptive models can be used in future drilling operations for DOS. The current drill job being carried out by system 100 can also use vibration analysis to make dynamic changes to operating parameters to mitigate vibrations. Step 280 of method 200 in Figure 2 provides an example of such real-time execution.The computer system 164 may be a nearby well site controller 160 or may be located remotely, such as in a cloud environment, data center, laboratory, or corporate office. The computer system 164 may be a laptop, smartphone, PDA, server, desktop computer, cloud computing system, other computing systems, or a combination thereof, which can be operated to perform the processes and methods described herein. Well site operators, engineers, and other personnel may send and receive data, instructions, measurements, and other information through 5. 1970156 of 18 various conventional means with the computer system 164 or the well site controller 160.

[0020] Figure 2 illustrates a flowchart of an example of a Method 200 for conducting a drilling operation carried out in accordance with the disclosure principles. Method 200 represents a vibration-centric approach when considering a new job design, enabling improved performance evaluation and cost reduction. Method 200 uses a dataset from at least one completed run of a drilling job to obtain vibration information for use in future drilling operations. An aggregated dataset from multiple runs can also be used to generate fleet-level analyses that can be used for one or more future drilling operations. Method 200 analyses can be used to make real-time decisions to mitigate vibrations or estimate tool performance against a job design.At least some of the steps of Method 200 can be performed using a vibration analyzer as disclosed herein. Method 200 begins after a drilling operation has been completed.

[0021] In step 210, drill job data is collected from a completed drill job. Drill job data includes sensor data collected from downhole sensors during the drill job. Sensor data can be raw data, and the sensors can be, for example, magnetometers, accelerometers, gyroscopes, etc. The sensors can be used to collect data on, for example, tension, compression, bending moment, and torque of a drill string. A sensor or sensors can also be used to measure the rotational speed of the shaft within a tool. The measured data can correspond to average values, a minimum value, a maximum value, or a combination of these. The raw data can be average data, peak data, or a combination of these. Statistics from the raw data can also be obtained, such as sticking and slippage data.Sticking and slippage data can be determined by the maximum revolutions per minute (RPM) minus the minimum RPM divided by the average RPM. Drilling job data may also include other post-run data, such as field data from the drilling job (e.g., surface data, surface drilling parameters used during the drilling job, RT, observations during the drilling run, etc.), formation information (e.g., geographic data, geological information, time and / or sonic logs). 1970156 of 18 depth), tool information (e.g., BHA information, sensor types, tool failure information, and maintenance information), job information (e.g., well plan information, operational information), and performance metrics (rate of penetration (ROP), angular change severity (DLS), M / LWD log quality). Drilling job data from more than one completed drill run can be collected and received.

[0022] In step 220, at least some of the collected data is processed. Sensor data can be processed to quantify the vibrations measured during drilling operations. Vibration data can be quantified based on predetermined thresholds for amplitude and / or time. For example, a vibration severity index can be determined from the sensor data and can be based on a vibration amplitude greater than a threshold amplitude or a vibration duration greater than a time threshold. The vibration severity index can be determined by grouping the sensor data based on frequencies, determining a magnitude for each grouping, and calculating the vibration severity index based on an integral of each magnitude. Figure 3 provides an example of a flowchart for determining a vibration severity index.

[0023] In addition, statistical information can be extracted from various sensor data. For example, statistical information such as a moving average, standard deviation, first derivatives, and the moving average of the derivatives can be determined for the various sensors. Vibration aggregation / classification based on thresholds can also be determined. A grouping similar to that used to determine the vibration severity index can be used to classify vibrations, where the classifications are selective aggregates that can be used only when certain thresholds are met. The classification of characteristic vibrations can be derived from specific magnitude measurements or changes in magnitude. The characteristic vibration classification can be determined from the analysis of sensor data over a period of time or depth.An example is the peak / average accelerometer data in the X, Y, or Z directions, based on the physical or numerical understanding of the different vibration modes. By definition, vibration modes can also be identified from sensor data based on vibration frequency ranges.

[0024] For example, processed raw sensor data are then available 1970156 of 18 of a time period. Moving averages and standard deviations can therefore be calculated from the processed data, and statistical metrics can also be used on the processed data to quantify vibrations. Aggregation and threshold-based classification is an example of quantification.

[0025] In step 230, a data pool is created from at least some of the drill job data and the processed data. The data pool can be a data lake that includes a combination of the drill job data and the processed data, or at least some of the drill job data and the processed data. The data pool allows combining sensor data with related drill job information to leverage the cause-and-effect correlation of vibrations for DOS.The data pool can be stored in a memory, such as a computer system memory 164. The data pool may include, for example: (1) geography / formation information, (2) BHA / sensor / well plan information, (3) operational information, observations during drilling, (4) surface drilling parameters used during the drilling run, (5) performance metrics (e.g., ROP, DLS, M / LWD log quality), (6) extracted vibration indices, (7) vibration mechanism as a function of time and depth, (8) time / depth sonic logs and / or geological information, and (9) tool failure and maintenance information.

[0026] A dataset is generated in step 240 from the data pool. A dataset can be generated by combining at least some of the drill job data, vibration severity index, and characteristic vibration ratings. The dataset can also include, for example, job information, training information, operating data, tooling information, performance measurements, and custom logs for the drill run. The dataset can include one or more vibration mechanism indices, vibration ratings, BHA configurations, performance measurements, and custom logs that can be automatically generated. Custom logs can be predefined based on the BHA / drill string configurations and surface systems for specific drill operations.An example of a vibration mechanism index derived from the data pool is illustrated in Figure 4.

[0027] The dataset can be generated for each drilling job and job-level analysis can be carried out for the drilling run in 8 1970156 of 18 particular. An aggregated dataset from multiple drilling runs can be used to generate fleet-level analysis. With a specific set of parameters entered, further data sharding can be achieved to understand similarities and differences. For example, is there vibration if a particular tool is used with a certain weight on bit (WOB)?

[0028] In step 250, at least one adaptive model is extracted from the dataset. Adaptive models can be used to determine the effectiveness of DOS for the drilling job and to advise on future well planning and execution designs. Multiple adaptive models can be extracted that target different parts of a drilling job. For example, one of the adaptive models from the dataset correlates characteristic vibrations with particular components associated with the drilling job. Adaptive models can therefore relate vibration information to the drilling job. Other types of data or models can also be generated to enhance the analysis. For example, a model can be generated that provides more granularity to the vibration classifications for drill bit-rock interaction. Different approaches can be used to obtain the adaptive models.For example, you can use a rules-based approach, machine learning, object code identification, or a combination of these to extract adaptive models from the dataset.

[0029] A rules-based approach can be used to correlate certain vibration events using drilling tools or parameters. Regarding machine learning, various supervised and unsupervised machine learning techniques can be used. For example, supervised machine learning techniques such as regression, random forests, or support vector machines can be used to enable decision-making and create adaptive models. Unsupervised machine learning techniques, such as various types of cluster analysis, can be used to gain less obvious insights from the dataset. The object code can be based on custom logs for each operation and can be used to identify patterns in the dataset. Custom logs can be used to recognize a relationship between one or more vibrations and a specific parameter.For example, an identified characteristic vibration can be used to recognize other characteristic vibrations. Regardless of the process, the dataset is analyzed to derive rules and detect patterns in search of important insights. 9. 1970156 of 18 can use machine learning to develop custom records from the analysis of the data reserve.

[0030] Adaptive models can also be generated based on initial models. By definition, initial models can be applied to adaptive models in stage 255. Initial models can be models based on simple rules used to generate and / or modify adaptive models. Initial models can also be heuristic models developed by users. While these types of models can be useful, models developed from the dataset, for example, through machine learning, can advantageously determine possible vibration information and correlations that human-developed heuristics might miss.

[0031] In stage 260, the correlation between vibration and DOS information for a drilling operation is carried out based on at least one or more adaptive models. When considering a new job design, a vibration-centric approach can be adopted by using one or more of the models when selecting, for example, a BHA configuration or drilling parameters to be used during the drilling operation. A combination of information, such as the vibration mechanism index, vibration severity, characteristic vibration mode classification, automatic custom logs, and job-related information used to develop the adaptive models, can be used for the correlation.

[0032] The use of one or more adaptive models can be achieved in several ways. For example, vibration information can be correlated with different aspects of DOS. The different correlations include: (1) BHA versus vibration, (2) training versus vibration, (3) performance versus vibration, (4) vibration modes versus maintenance, and (5) DOS plan versus execution. The adaptive models generated in stage 250 can be further adapted based on the correlations.

[0033] In stage 270, a DOS for a drilling operation can be developed based on one or more correlations from at least one or more adaptive models. From these correlations, a data-driven DOS can be determined, enabling cost reduction and improved performance evaluation. Potential vibration damage to drilling equipment can be prevented with a service design based on the 10 1970156 of 18 correlations.

[0034] Additionally, at least a portion of the drilling operation is performed in stage 280 based on vibration analysis, such as one or more correlations from at least one adaptive model. The analysis provided by the correlations can be used to make real-time decisions to mitigate vibrations or estimate tool performance against the design for the drilling operation. The correlations can be used to provide expected vibrations, recommended drilling parameters, directional guidance (well trajectory planning), tool life estimation, and anticipated formation time / depth / BHA performance.The drilling operation can then be carried out using, for example, recommended drilling parameters, by replacing or not replacing the drill bit based on its estimated lifespan, adjusting the drill bit's direction according to the steerage advice, and so on. Vibrations can be mitigated by changing operating parameters such as ROP, WOB, RPM, or a combination of one or more of these or other operating parameters. The changes can be decreases or increases and may differ depending on the type of mitigation.

[0035] Figure 3 illustrates a flowchart of an example of a Method 300 for determining a vibration severity index carried out in accordance with the disclosure principles. Method 300 is directed toward a single drill job that is completed but can be repeated for multiple drill jobs. A computer system, such as the 164 computer system or a downhole computer system, can be used to carry out at least some part of Method 300. At least some part of Method 300 can be carried out downhole. Method 300 begins in step 310 with raw sensor data collected from downhole sensors during a drill job. The sensor data can be high-frequency data collected during a drill run from downhole sensors such as magnetometers, accelerometers, and gyroscopes.Sensor data can be sent in bursts, such as at 1000 Hertz.

[0036] In stage 320, the sensor data is processed to convert the raw data into discrete data. A fast Fourier transform (FFT) can be used. The sensor data can be processed continuously as it is received.

[0037] The processed data are placed into data clusters in stage 330. 1970156 of 18 By definition, raw sensor data is grouped according to frequency. Processed data can represent a magnitude of an amplitude of raw sensor data, where a dominant amplitude is identified in frequency responses. Each grouping represents a frequency range, and the number of groupings can be predetermined based on, for example, historical data, or they can be determined dynamically and adjusted based on the received sensor data. The groupings correspond to different sensors and provide vibration information in particular directions based on the sensors' orientation.

[0038] In step 340, a determination is made as to whether a value in each grouping exceeds a threshold. The threshold can be set based on historical data and may differ for one or more of the groupings. When a value exceeds a threshold, it is recorded. Therefore, while raw sensor data can be continuously processed, not all values ​​are saved. Consequently, memory space can be reduced, especially downhole.

[0039] An integral of the magnitudes for each grouping is calculated in step 350. Conventional methods can be used to determine the integral. A vibration severity index is generated in step 360 based on the integral of the magnitudes present in the groupings. The integrals for each grouping are used as an index value for the index.

[0040] At least part of methods 200 and 300 may represent an algorithm or algorithms and be encapsulated in software code or hardware, for example, an application, a computer library, a dynamic-link library, a module, a function, RAM, ROM, and other software and hardware implementations. The software may be stored in a file, database, or other computer system storage mechanisms. At least part of methods 200 and 300 may be implemented partly in software and partly in hardware. A processor may be directed to perform operations according to the algorithms.

[0041] Figure 4 illustrates an example of a vibration mechanism index 400 from a dataset generated from a data pool in accordance with disclosure principles. The vibration mechanism index 400 is represented in a table having three columns for the mechanism 410, the vibration mode 420, and the expected frequency range 430. The vibration mode types are listed in column 12. 1970156 of 18 The vibration mechanisms in column 420 can be determined by processing sensor data obtained from a drilling job. For example, sensor data collected in step 210 of method 200 can be processed in step 220 to determine different types of vibration modes. Drilling job data collected in step 210, including sensor data, can also be processed in step 220 to provide the expected frequency ranges for column 430. The vibration mechanisms in column 1 can be determined from the analysis of the data pool from step 230 and the resulting dataset generated in step 240. The vibration mechanisms in column 410 can be determined automatically by a processor. For example, sensor data can be analyzed, and the different vibration modes can be inferred from the frequencies and amplitudes, such as the dominant amplitudes.An analysis of a combination of data, such as steps 240 to 260 of Method 200, can be performed on the data collected from the data pool in step 230 to determine vibration mechanisms. The logic for determining these vibration mechanisms can be integrated with a downhole tool to mitigate vibrations during a drilling operation by changing operating parameters.

[0042] Figure 5 illustrates a block diagram of an example of a Vibration Analyzer 500 constructed in accordance with the principles of this disclosure. The Vibration Analyzer 500 can be implemented in a computer device or computer system that includes the logic and memory necessary to perform the functions disclosed herein, such as at least a portion of Methods 200 and 300. The Vibration Analyzer 500 can be implemented in a computer system at a well site and can be used to make real-time changes to a drilling operation based on the analysis of vibration information from the drilling operation. The Vibration Analyzer 500 can be integrated with downhole tools, such as as part of a BHA, and direct a change in operating parameters based on the vibration analysis.A Vibration Analyzer 500 can also be implemented on a computer system at the drilling rig, dynamically adjusting one or more operating parameters based on the vibration analysis. In some cases, the Vibration Analyzer 500, or at least a portion of it, can be implemented on a server.

[0043] The vibration analyzer 500 includes at least one interface, for example, the communications interface 510, at least one memory 520 (or storage of 13 1970156 of 18 data) that stores data and computer programs, and at least one 530 processor that performs functions when directed by computer programs. Interface 510 is a component or device interface configured to communicate (transmit and receive) data. As illustrated, interface 510 can receive data from drilling jobs and output a DOS, a change in operating parameters, or both after analysis by processor 530. Interface 510 can be a conventional interface that communicates data according to standard protocols. Memory 520 is configured to store a series of operating instructions that direct the operation of processor 530 when it starts, including code representing the algorithms for vibration analysis as disclosed herein, such as the extraction of adaptive models and correlative vibration information.The code may respond to algorithms that represent at least some of the steps of methods 200 and 300. Memory 520 may also store sensor data from the current drilling operation. Memory 520 may also include a data reserve, as shown in Figure 2. Memory 520 is a non-transient, computer-readable medium. Memory 520 may be server memory.

[0044] The 530 processor is configured to direct a drilling operation based on vibration information from one or more previous drilling jobs. By definition, the 530 processor includes the logic necessary to communicate with the 510 interface and the 520 memory and to perform the functions described herein to carry out a drilling operation. For example, the 530 processor can analyze vibration information and generate a DOS based on this information. The 530 processor can also determine a change in operating parameters based on the vibration analysis. The 530 processor can be part of a server.

[0045] A portion of the apparatus, systems, or methods described above may be implemented in various forms or carried out by various analog or digital data processors, wherein the processors are programmed or stored with executable programs of software instruction sequences to perform one or more of the steps of the methods. For example, a processor may be a programmable logic device, such as a programmable logic array (PAL), a generic logic array (GAL), a field-programmable gate array (FPGA), or another type of computer processing device (CPD). The software instructions of such programs may represent algorithms and be encoded in machine-executable form on non-digital storage media. 1970156 of 18 transient digital data, for example, magnetic or optical disks, random access memory (RAM), magnetic hard disks, flash memories and / or read-only memory (ROM), to allow various types of digital data processors or computers to perform one, multiple or all of the stages of one or more of the methods, or functions, systems or apparatus described above herein.

[0046] The disclosed example portions or embodiments may refer to computer storage products with a non-transient, computer-readable medium that has program code thereon to perform various computer-implemented operations that represent a part of an apparatus or device or perform steps of a method set forth herein. The term "non-transient" as used herein refers to all computer-readable media except transient and propagating signals. Examples of non-transient, computer-readable media include, but are not limited to: magnetic media, such as hard disks, floppy disks, and magnetic tape; optical media, such as CD-ROM discs; magneto-optical media, such as floppy disks; and hardware devices that are specially configured to store and execute program code, such as ROM and RAM devices.Configured means, for example, designed, built, or programmed with the logic and / or features necessary to perform a task or tasks. Therefore, a configured device is capable of performing the task or tasks. Examples of program code include machine code, such as that produced by a compiler, and files containing higher-level code that can be executed by a computer using an interpreter.

[0047] In interpreting the disclosure, all terms should be interpreted as broadly as possible in accordance with the context. In particular, the expressions "includes" and "comprising" should be interpreted as referring to elements, components, or steps in a non-exclusive manner, indicating that the elements, components, or steps mentioned may be present, or used or combined with other elements, components, or steps not expressly referenced.

[0048] Those in the mid-level trade to whom this request is addressed will appreciate that additions, deletions, substitutions, and modifications to the described embodiments are possible. It should also be understood that the terminology used herein is for the purpose of describing only particular embodiments and is not intended to be exhaustive, because the scope 15 The scope of this disclosure, as defined in section 1970156 of the 18th paragraph, is limited only by the claims. Unless otherwise specified, all technical and scientific terms used herein have the same meaning as commonly understood by persons of the middle trade to whom this disclosure pertains. Although any methods or materials similar or equivalent to those described herein may also be used in the practice or analysis covered by this disclosure, only a limited number of example methods and materials are described herein.

[0049] Each of the aspects disclosed in the SYNTHESIS may have one or more of the following additional elements in combination.

[0050] Element 1: further comprising classifying characteristic vibrations using the vibration severity index and drilling job data. Element 2: further comprising generating a dataset by combining at least some of the drilling job data, the vibration severity index, and characteristic vibration classifications, and extracting at least one adaptive model from the dataset, wherein the execution of at least a portion of the drilling operation includes the use of adaptive models. Element 3: wherein the execution includes real-time adjustments based on the correlations of the adaptive models. Element 4: wherein the extraction includes the use of machine learning to extract at least one adaptive model from the dataset.Element 5: wherein at least one adaptive model of the dataset correlates characteristic vibrations with particular components associated with the drilling operation. Element 6: wherein the execution uses the correlation between characteristic vibrations and particular components to select a service design for the drilling operation. Element 7: wherein the determination of the vibration severity index includes grouping sensor data based on frequencies, determining a magnitude for each grouping, and calculating the vibration severity index based on an integral of each magnitude. Element 8: wherein the drilling operation data further include more than one of the drilling operation field data, formation information associated with the drilling operation, tool information, job information, and performance metrics.Element 9: further comprising creating a data pool from drilling job data and processed sensor data, wherein the processed data includes the vibration severity index. Element 10: further comprising automatically generating an index of 16. Element 11: Wherein the processor uses machine learning to extract at least one adaptive model from the dataset, which includes the vibration mechanism index. Element 12: Wherein the processor is configured to perform at least a portion of the drilling operation in real time. Element 13: Wherein the vibration information includes the vibration mode classification, a vibration severity index, and statistical metrics from the sensor data. Element 14: Wherein the processor uses machine learning to extract at least one adaptive model from the dataset.Element 15: wherein the multiple downhole tools include a drill bit and the processor is configured to direct the operation of the drill bit by implementing a change in at least one of the weight on the bit, revolutions per minute, and rate of penetration based on at least one adaptive model. Element 16: wherein the processor is further configured to generate a service design for the drilling operation based on at least one adaptive model. Element 17: wherein the dataset includes a vibration mechanism index comprising various vibration mechanisms for different vibration modes and a frequency range for the various vibration mechanisms and different vibration modes. 1970156 of 18 CLARKE MODET & CO. (ARGENTINA) SA - 30540437455 Digitally signed by PORTALTRAMITES - INPI Date: 2022.09.27 13:30:13 -03:00 Reason: Digitally Signed by the INPI Location: Buenos Aires, Argentina 1970156

Claims

1. A method for carrying out a drilling operation, characterized in that it comprises: collecting drilling job data from a completed drilling job, wherein the drilling job data includes sensor data collected from downhole sensors; determining a vibration severity index from the sensor data, wherein determining includes grouping the sensor data based on frequencies and determining a magnitude for each grouping; and carrying out a drilling operation which includes operating a drill bit; identifying, using the vibration severity index, at least one component of the drilling operation that causes vibrations during the operation of the drill bit; mitigating the vibrations by adjusting at least one operating parameter of the component; and continuing the execution of the drilling operation after mitigation. Nineteen claims follow.